Build technology intelligence systems that turn large-scale data into strategic judgment. Sessions are designed as executable playbooks with objectives, tasks, and deliverables.
Pipeline board
Signals
Capture evidence streams
Representation
Model text and topics
Structure
Map networks and regimes
Dynamics
Track growth and shifts
Judgment
Decide with guardrails
Design principle
Every session includes tasks, readings, and deliverables.
Atomic unit: the session plan.
Design principle
Signals, representation, structure, dynamics, judgment.
Layered evidence and synthesis.
Design principle
Critique checkpoints and portfolio synthesis.
Milestones tied to artifacts.
Design principle
Instructor + 3 TAs with lab and studio coverage.
Weekly lab facilitation.
This graduate course trains students to collect large-scale technology data, map ecosystems, model trajectories, and translate evidence into strategic technology portfolios.
Atomic unit
The session
Evidence base
WoS, Scopus, Derwent, Lens
16 sessions, 3 hours each, with TA-led labs.
8 online labs focused on data collection and modeling tools.
Python (Colab/Jupyter), Gephi, WoS, Scopus, Derwent, Lens.
| Component | Weight |
|---|---|
| Session deliverables (Sessions 1-15) | 45% |
| TA practicum exercises | 15% |
| Capstone project (Session 16) | 35% |
| Participation & peer review | 5% |
Prepare
Readings + data tasks
Execute
Lecture + TA lab
Deliver
Submission + critique
Reflect
Update evidence + portfolio logic